AIエージェントをつくる-59：８章：CodexとCladueCodeにLLMクロスレビューをさせてみた 第2340回

This episode demonstrates implementing LLM cross-review functionality between Codex and Claude Code, where two AI models review each other's code outputs. The creator, with basic Python skills, explores how to leverage multiple LLMs for quality assurance and code validation in AI agent development. This represents an advanced technique for improving code reliability through automated peer review between different language models.

Key Points

• •Implement cross-review mechanism where Codex and Claude Code evaluate each other's generated code
• •Use multiple LLMs as quality gates to catch errors and inconsistencies that single models might miss
• •Design review prompts that guide each model to provide constructive feedback on the other's output
• •Establish clear criteria for code acceptance based on cross-model consensus
• •Integrate LLM cross-review into the AI agent development workflow for continuous code validation
• •Compare output quality and review accuracy between different model pairs
• •Document review results to identify which model pairs work best for specific code types
• •Create feedback loops where review comments are fed back to improve subsequent code generation

Workflow Diagram

Concepts